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36 results on '"Zeb, Asim"'

2. An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique.

Author

Kiran, Lubna, Zeb, Asim, Ur Rehman, Qazi Nida, Rahman, Taj, Khan, Muhammad Shehzad, Ahmad, Shafiq, Irfan, Muhammad, Naeem, Muhammad, Huda, Shamsul, and Mahmoud, Haitham

Subjects
MAGNETIC resonance imaging, BRAIN tumors, CONVOLUTIONAL neural networks, DEEP learning
Abstract

Neuroscience is a swiftly progressing discipline that aims to unravel the intricate workings of the human brain and mind. Brain tumors, ranging from non-cancerous to malignant forms, pose a significant diagnostic challenge due to the presence of more than 100 distinct types. Effective treatment hinges on the precise detection and segmentation of these tumors early. We introduce a cutting-edge deep-learning approach employing a binary convolutional neural network (BCNN) to address this. This method is employed to segment the 10 most prevalent brain tumor types and is a significant improvement over current models restricted to only segmenting four types. Our methodology begins with acquiring MRI images, followed by a detailed preprocessing stage where images undergo binary conversion using an adaptive thresholdingmethod and morphological operations. This prepares the data for the next step, which is segmentation. The segmentation identifies the tumor type and classifies it according to its grade (Grade I to Grade IV) and differentiates it from healthy brain tissue. We also curated a unique dataset comprising 6,600 brain MRI images specifically for this study. The overall performance achieved by our proposed model is 99.36%. The effectiveness of our model is underscored by its remarkable performance metrics, achieving 99.40% accuracy, 99.32% precision, 99.45% recall, and a 99.28% F-Measure in segmentation tasks. [ABSTRACT FROM AUTHOR]

Published
2024
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3. IoT-Enabled Secure and Scalable Cloud Architecture for Multi-User Systems: A Hybrid Post-Quantum Cryptographic and Blockchain based Approach Towards a Trustworthy Cloud Computing

Author

Irshad, Reyazur Rashid, primary, Hussain, Shahid, additional, Hussain, Ihtisham, additional, Nasir, Jamal Abdul, additional, Zeb, Asim, additional, Alalayah, Khaled M., additional, Alattab, Ahmed Abdu, additional, Yousif, Adil, additional, and Alwayle, Ibrahim M., additional

Published
2023
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10. Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks.

Author

Rahman, Taj, Ahmad, Irfan, Zeb, Asim, Khan, Inayat, Ali, Gauhar, and ElAffendi, Mohammed

Subjects
WIRELESS sensor networks, NETWORK routing protocols, TECHNOLOGICAL innovations, END-to-end delay, ENERGY consumption
Abstract

Underwater Wireless Sensor Networks (UWSNs) are emerging technology for disclosing multiple applications, such as oil, earth quick, and marine environments. All sensor nodes deployed in UWSNs operate through limited power batteries. Prolonging the network's lifetime in such environments is an essential task and a hot topic among researchers. Multiple routing protocols have been designed to overcome the limited power issue and reduce energy consumption. Each routing protocol evaluated different parameters, but the issue is still unclear as to which one is better. In this study, we evaluated multiple routing protocols to investigate which is better in terms of parameters, such as packet delivery ratio (PDR), energy consumption, end-to-end delay, and the number of alive nodes. The simulation results indicate that Reliability and Adaptive Cooperation for Efficient UWSNs Using Sink Mobility (RACE-SM) performs better in all performance metrics than other routing schemes. [ABSTRACT FROM AUTHOR]

Published
2023
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25. Resource Allocation and Optimization in Device-to-Device Communication 5G Networks.

Author

Al-Zahrani, Fahad Ahmed, Khan, Imran, Zareei, Mahdi, Zeb, Asim, and Waheed, Abdul

Subjects
TELECOMMUNICATION systems, RESOURCE allocation, POWER resources, ENERGY consumption, POWER spectra, 5G networks, MULTICASTING (Computer networks), FRACTIONAL programming
Abstract

The next-generationwireless networks are expected to provide higher capacity, system throughput with improved energy efficiency. One of the key technologies, to meet the demand for high-rate transmission, is device-to-device (D2D) communication which allows users who are close to communicating directly instead of transiting through base stations, and D2D communication users to share the cellular user chain under the control of the cellular network. As a new generation of cellular network technology, D2D communication technology has the advantages of improving spectrum resource utilization and improving system throughput and has become one of the key technologies that have been widely concerned in the industry. However, due to the sharing of cellular network resources, D2D communication causes severe interference to existing cellular systems. One of the most important factors in D2D communication is the spectrum resources utilization and energy consumption which needs considerable attention from research scholars. To address these issues, this paper proposes an efficient algorithm based on the idea of particle swarm optimization. The main idea is to maximize the energy efficiency based on the overall link optimization of D2Duser pairs by generating an allocationmatrix of spectrum and power. The D2D users are enabled to reuse multiple cellular user's resources by enhancing their total energy efficiency based on the quality of service constraints and the modification of location and speed in particle swarm. Such constraint also provides feasibility to solve the original fractional programming problem. Simulation results indicate that the proposed scheme effectively improved the energy efficiency and spectrum utilization as compared with other competing alternatives. [ABSTRACT FROM AUTHOR]

Published
2021
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